Although much progress has been made toward understanding the biological basis of cancer and in its diagnosis and treatment, it is still one of the leading causes of death in the United States. Inherent difficulties in the diagnosis and treatment of cancer include among other things, the existence of many different subgroups of cancer and the concomitant variation in appropriate treatment strategies to maximize the likelihood of positive patient outcome.
Subclassification of cancer has typically relied on the grouping of tumors based on tissue of origin, histology, cytogenetics, immunohistochemistry, and known biological behavior. The pathologic diagnosis used to classify the tumor taken together with the stage of the cancer is then used to predict prognosis and direct therapy. However, current methods of cancer classification and staging are not completely reliable.
Gene expression profiling using microarrays is likely to result in improvements in cancer classification and prediction of prognosis (Golub, 1999; Perou, 2000; Hedenfalk, 2001; Khan, 2001). Still, the wealth of information garnered using microarrays has, thus far, not yielded effective clinical applications. Global expression analysis has led to the development of sophisticated computer algorithms seeking to extend data analysis beyond simple expression profiles (Quackenbush, 2001; Khan, 2001). At this time, however, no clear consensus exists regarding which computational tools are optimal for the analysis of large gene expression profiling data sets, particularly in the clinical setting. Moreover, many of these bioinformatics tools under development and testing are quite complex leaving the practical use of microarray data beyond the scope of many biomedical scientists and/or clinicians. With rare exceptions (e.g. PSA and prostate cancer), it is generally assumed that expression levels of any one gene are insufficient in the diagnosis and/or prognosis of cancer. However, it is equally erroneous to assume a priori that the expression profiles of large numbers of genes are explicitly required for this purpose.
It is difficult to predict from standard clinical and pathologic features the clinical course of cancer. However, it is very important in the treatment of cancer to select and implement an appropriate combination of therapeutic approaches. The available methods for designing strategies for treating cancer patients are complex and time consuming. The wide range of cancer subgroups and variations in disease progression limit the predictive ability of the healthcare professional. In addition, continuing development of novel treatment strategies and therapeutics will result in the addition of more variables to the already complex decision-making process involving matching the cancer patient with a treatment regimen that is appropriate and optimized for the cancer stage, tumor growth rate, and other factors central to the individual patient's prognosis. Because of the critical importance of selecting appropriate treatment regimens for cancer patients, the development of guidelines for treatment selection is of key interest to those in the medical community and their patients. Thus, there presently is a need for objective, reproducible, and sensitive methods for diagnosing cancer, predicting cancer patient prognosis and outcome, and selecting and monitoring optimal treatment regimens.